cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:May 21, 2025License:cc-by-nc-4.0Architecture:Transformer Open Weights Cold

The cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct is a 7.6 billion parameter instruction-tuned model based on the Qwen2.5-Coder architecture, specifically designed for Text-to-SQL generation. It implements the CSC-SQL method, which integrates Self-Consistency and Self-Correction, fine-tuned with Group Relative Policy Optimization (GRPO). This model excels at translating natural language questions into SQL queries, achieving 71.72% execution accuracy on the BIRD private test set for its 7B variant.

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CSC-SQL: Enhanced Text-to-SQL Generation

This model, cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct, is a 7.6 billion parameter instruction-tuned variant of the Qwen2.5-Coder architecture, specifically optimized for Text-to-SQL tasks. It is based on the novel CSC-SQL method, which addresses limitations in traditional Self-Consistency and Self-Correction techniques for SQL generation.

Key Capabilities

  • Integrated Self-Consistency and Self-Correction: CSC-SQL combines these two approaches to select optimal outputs and correct both syntactic and semantic errors in generated SQL queries.
  • Reinforcement Learning Fine-tuning: Utilizes the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models, significantly enhancing output quality.
  • High Accuracy: Achieves a notable 71.72% execution accuracy on the BIRD private test set for the 7B model, demonstrating strong performance in complex Text-to-SQL scenarios.
  • Robust Error Handling: Designed to overcome issues where Self-Consistency might select suboptimal outputs or Self-Correction only addresses syntactic errors.

Good For

  • Translating Natural Language to SQL: Ideal for applications requiring accurate conversion of user questions into executable SQL queries.
  • Database Interaction: Useful for developers building tools that allow natural language querying of relational databases.
  • Improving SQL Generation Reliability: Provides a robust solution for generating high-quality SQL by integrating advanced self-correction mechanisms.

This model is part of a collection of CSC-SQL models, with its code open-sourced for further research and development.

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